Multi-Cellular And Multi-Scale Approaches For Cartilage Repair

To date, there are no surgical interventions that can fully restore damaged cartilage. While the field of tissue engineering has emerged as a potential solution, there exist numerous shortcomings in existing technologies. This thesis addresses some of these shortcomings by developing multi-cellular and multi-scale approaches to advance technologies for cartilage repair. For instance, co-culture systems have recently been introduced as a novel tissue engineering strategy for cartilage repair. We show here that a small pool of young/healthy chondrocytes (CH) can rejuvenate a larger pool of older/infirm mesenchymal stem cells (MSC), improving matrix deposition while suppressing MSC hypertrophic conversion. This resolves the deficiency of CH supply as well as phenotypic instability and age-related decline in potential of MSCs. Biomimetic design in cartilage tissue engineering is yet another challenge, given the complexity of the native tissue. While articular cartilage is a highly stratified tissue with depth-dependent properties, most cell-laden cartilage constructs described in the literature simply recapitulate bulk functional properties without morphologic recapitulation. Here, we develop a layer-by-layer fabrication strategy to promote depth dependent tissue formation and function. We also introduce a novel method to properly transport nutrients and wastes into/out of engineered constructs. Finally, another equally important aspect in designing engineered cartilage tissue is clinical translation. In this, the simplicity of application, including ease of handling and preparation, and methods for implantation in situ, are required for clinical translation. To address this, we developed a micro-scale co-culture system that can be easily fabricated and delivered into cartilage defects in the context of current clinical cartilage repair procedures. Collectively, the thesis advances our understanding of the chondrogenic capacity of MSCs, introduces various methods to improve MSC chondrogenesis (using chemical and natural factors), and identifies some of the underlying mechanisms regulating this process. Further, this work introduces novel methods to fabricate engineered cartilage to mimic the structure of native tissue as well as clinical relevant methods to couple tissue engineering efforts with current clinical practice. These innovative approaches may aid the many patients who suffer from cartilage disease by enhancing cartilage tissue engineering strategies and reducing them to clinical practice

Revisiting Self-Training with Regularized Pseudo-Labeling for Tabular Data

Recent progress in semi- and self-supervised learning has caused a rift in the long-held belief about the need for an enormous amount of labeled data for machine learning and the irrelevancy of unlabeled data. Although it has been successful in various data, there is no dominant semi- and self-supervised learning method that can be generalized for tabular data (i.e. most of the existing methods require appropriate tabular datasets and architectures). In this paper, we revisit self-training which can be applied to any kind of algorithm including the most widely used architecture, gradient boosting decision tree, and introduce curriculum pseudo-labeling (a state-of-the-art pseudo-labeling technique in image) for a tabular domain. Furthermore, existing pseudo-labeling techniques do not assure the cluster assumption when computing confidence scores of pseudo-labels generated from unlabeled data. To overcome this issue, we propose a novel pseudo-labeling approach that regularizes the confidence scores based on the likelihoods of the pseudo-labels so that more reliable pseudo-labels which lie in high density regions can be obtained. We exhaustively validate the superiority of our approaches using various models and tabular datasets.Comment: 10 pages for the main part and 8 extra pages for the appendix. 2 figures and 3 tables for the main par

CAST: Cluster-Aware Self-Training for Tabular Data

Self-training has gained attraction because of its simplicity and versatility, yet it is vulnerable to noisy pseudo-labels. Several studies have proposed successful approaches to tackle this issue, but they have diminished the advantages of self-training because they require specific modifications in self-training algorithms or model architectures. Furthermore, most of them are incompatible with gradient boosting decision trees, which dominate the tabular domain. To address this, we revisit the cluster assumption, which states that data samples that are close to each other tend to belong to the same class. Inspired by the assumption, we propose Cluster-Aware Self-Training (CAST) for tabular data. CAST is a simple and universally adaptable approach for enhancing existing self-training algorithms without significant modifications. Concretely, our method regularizes the confidence of the classifier, which represents the value of the pseudo-label, forcing the pseudo-labels in low-density regions to have lower confidence by leveraging prior knowledge for each class within the training data. Extensive empirical evaluations on up to 20 real-world datasets confirm not only the superior performance of CAST but also its robustness in various setups in self-training contexts.Comment: 17 pages with appendi

A Method of Discharge Estimation Based on Lateral Velocity Distribution Function Applied To Ultrasonic Velocity Meter System

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection

Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the large domain gap between the two sensors in terms of coordinates and data distribution when fusing their features. In this paper, we propose a novel camera-LiDAR fusion architecture called, 3D Dual-Fusion, which is designed to mitigate the gap between the feature representations of camera and LiDAR data. The proposed method fuses the features of the camera-view and 3D voxel-view domain and models their interactions through deformable attention. We redesign the transformer fusion encoder to aggregate the information from the two domains. Two major changes include 1) dual query-based deformable attention to fuse the dual-domain features interactively and 2) 3D local self-attention to encode the voxel-domain queries prior to dual-query decoding. The results of an experimental evaluation show that the proposed camera-LiDAR fusion architecture achieved competitive performance on the KITTI and nuScenes datasets, with state-of-the-art performances in some 3D object detection benchmarks categories.Comment: 12 pages, 3 figure

Realization of Non-Hermitian Hopf Bundle Matter

Line excitations in topological phases are a subject of particular interest because their mutual linking structures encode robust topological information of matter. It has been recently shown that the linking and winding of complex eigenenergy strings can classify one-dimensional non-Hermitian topological matter. However, in higher dimensions, bundles of linked strings can emerge such that every string is mutually linked with all the other strings. Interestingly, despite being an unconventional topological structure, a non-Hermitian Hopf bundle has not been experimentally clarified. Here, we make the first attempt to explore the non-Hermitian Hopf bundle by visualizing the global linking structure of spinor strings in the momentum space of a two-dimensional electric circuit. By exploiting the flexibility of reconfigurable couplings between circuit nodes, we can study the non-Hermitian topological phase transition and gain insight into the intricate structure of the Hopf bundle. Furthermore, we find that the emergence of a higher-order skin effect in real space is accompanied by the linking of spinor strings in momentum space, revealing a bulk-boundary correspondence between the two domains. The proposed non-Hermitian Hopf bundle platform and visualization methodology pave the way to design new topologically robust non-Hermitian phases of matter

Attenuating the EGFR-ERK-SOX9 axis promotes liver progenitor cell‐mediated liver regeneration in zebrafish

The liver is a highly regenerative organ, but its regenerative capacity is compromised in severe liver injury settings. In chronic liver diseases, the number of liver progenitor cells (LPCs) correlates proportionally to disease severity, implying that their inefficient differentiation into hepatocytes exacerbates the disease. Moreover, LPCs secrete pro‐inflammatory cytokines; thus, their prolonged presence worsens inflammation and induces fibrosis. Promoting LPC‐to‐hepatocyte differentiation in patients with advanced liver disease, for whom liver transplantation is currently the only therapeutic option, may be a feasible clinical approach since such promotion generates more functional hepatocytes and concomitantly reduces inflammation and fibrosis. Here, using zebrafish models of LPC‐mediated liver regeneration, we present a proof‐of‐principle of such therapeutics by demonstrating a role for the EGFR signaling pathway in differentiation of LPCs into hepatocytes. We found that suppression of EGFR signaling promoted LPC‐to‐hepatocyte differentiation via the MEK‐ERK‐SOX9 cascade. Pharmacological inhibition of EGFR or MEK/ERK promoted LPC‐to‐hepatocyte differentiation as well as genetic suppression of the EGFR‐ERK‐SOX9 axis. Moreover, Sox9b overexpression in LPCs blocked their differentiation into hepatocytes. In the zebrafish liver injury model, both hepatocytes and biliary epithelial cells contributed to LPCs. EGFR inhibition promoted the differentiation of LPCs regardless of their origin. Notably, short‐term treatment with EGFR inhibitors resulted in better liver recovery over the long term. Conclusion: The EGFR‐ERK‐SOX9 axis suppresses LPC‐to‐hepatocyte differentiation during LPC‐mediated liver regeneration. We suggest EGFR inhibitors as a pro‐regenerative therapeutic drug for patients with advanced liver disease

Effects of Mesenchymal Stem Cell and Growth Factor Delivery on Cartilage Repair in a Mini-Pig Model

Objective We have recently shown that mesenchymal stem cells (MSCs) embedded in a hyaluronic acid (HA) hydrogel and exposed to chondrogenic factors (transforming growth factor-3 [TGF-3]) produce a cartilage-like tissue in vitro. The current objective was to determine if these same factors could be combined immediately prior to implantation to induce a superior healing response in vivo relative to the hydrogel alone. Design Trochlear chondral defects were created in Yucatan mini-pigs (6 months old). Treatment groups included an HA hydrogel alone and hydrogels containing allogeneic MSCs, TGF-3, or both. Six weeks after surgery, micro-computed tomography was used to quantitatively assess defect fill and subchondral bone remodeling. The quality of cartilage repair was assessed using the ICRS-II histological scoring system and immunohistochemistry for type II collagen. Results Treatment with TGF-3 led to a marked increase in positive staining for collagen type II within defects (P 0.05). Neither condition had an impact on other histological semiquantitative scores (P > 0.05), and inclusion of MSCs led to significantly less defect fill (P 0.05). Conclusions At this early healing time point, treatment with TGF-3 promoted the formation of collagen type II within the defect, while allogeneic MSCs had little benefit. Combination of TGF-3 and MSCs at the time of surgery did not produce a synergistic effect. An in vitro precultured construct made of these components may be required to enhance in vivo repair in this model system

Effects of Mesenchymal Stem Cell and Growth Factor Delivery on Cartilage Repair in a Mini-Pig Model

We have recently shown that mesenchymal stem cells (MSCs) embedded in a hyaluronic acid (HA) hydrogel and exposed to chondrogenic factors (transforming growth factor–β3 [TGF-β3]) produce a cartilage-like tissue in vitro. The current objective was to determine if these same factors could be combined immediately prior to implantation to induce a superior healing response in vivo relative to the hydrogel alone